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STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting [article]

Cheonbok Park, Chunggi Lee, Hyojin Bahng, Taeyun won, Kihwan Kim, Seungmin Jin, Sungahn Ko, Jaegul Choo
2019 arXiv   pre-print
This paper proposes a novel Spatio-Temporal Graph Attention (STGRAT) that effectively captures the spatio-temporal dynamics in road networks.  ...  Predicting the road traffic speed is a challenging task due to different types of roads, abrupt speed changes, and spatial dependencies between roads, which requires the modeling of dynamically changing  ...  In this work, we propose a novel Spatio-Temporal Graph Attention Network (STGRAT) for predicting traffic speed, entirely based on a self-attention mechanism.  ... 
arXiv:1911.13181v1 fatcat:tbkzbzxsabci7ox3o62gqitwpa

STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting [article]

Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Chenxing Wang
2021 arXiv   pre-print
In this paper, we propose a novel deep learning model for traffic forecasting, named Multi-Context Aware Spatio-Temporal Joint Linear Attention (STJLA), which applies linear attention to the spatio-temporal  ...  Previous works combined graph convolution networks (GCNs) and self-attention mechanism with deep time series models (e.g. recurrent neural networks) to capture the spatio-temporal correlations separately  ...  In this paper, we propose a novel Multi-Context Aware Spatio-Temporal Joint Linear Attention Network (STJLA) for traffic forecasting, which design a spatio-temporal joint mode that combines the sub-graphs  ... 
arXiv:2112.02262v1 fatcat:e2bagkvdpjdu7awhyoig2pbxeu

Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network [article]

Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Chenxing Wang, Liang Zeng
2022 arXiv   pre-print
short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas; ii) the under-exploration of the graph  ...  Moreover, a novel wavelet-based graph positional encoding and a query sampling strategy are introduced in our spectral graph attention to effectively guide message passing and efficiently calculate the  ...  Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of IJCAI, 2018.  ... 
arXiv:2112.02740v2 fatcat:xbaudqqkbzhz5jjiva3gmkx33y

Spatial‐temporal attention wavenet: A deep learning framework for traffic prediction considering spatial‐temporal dependencies

Chenyu Tian, Wai Kin (Victor) Chan
2021 IET Intelligent Transport Systems  
Traffic prediction that aims to model the dynamic change of the traffic system is a well-studied spatial-temporal prediction problem, and multi-step traffic forecasting on road network is a crucial task  ...  To better capture the complex spatial-temporal dependencies and forecast traffic conditions on road networks, a multi-step prediction model named Spatial-Temporal Attention Wavenet (STAWnet) is proposed  ...  is an end-to-end solution for traffic forecasting that captures spatial, short-term, and long-term periodical dependencies. • ST-GRAT: Spatiao-Temporal GRaph ATtention [33] , which uses spatial attention  ... 
doi:10.1049/itr2.12044 fatcat:4fr5numrcjhulmtfa6pstwyxeu